From dynamic data to fuzzy state-space controllers: Methodology and applications

Author

Kroll, Andreas ; Bernd, Thomas

Author_Institution

ABB Corp. Res. Center, Heidelberg, Germany

fYear

1999

fDate

Aug. 31 1999-Sept. 3 1999

Firstpage

3563

Lastpage

3568

Abstract

This contribution presents a closed methodology to derive fuzzy state space controllers from dynamic process data: Sugeno-type fuzzy models with multivariate membership functions in I/O representation are identified by means of fuzzy clustering, LS and optimization methods. An equivalent fuzzy state-space representation is derived. Employing that a fuzzy state-space controller is desgined. To compensate for steady state errors an adaptive set point filter is calculated. The concept is applied in two case studies including an industrial hydraulic linear drive.

Keywords

adaptive filters; fuzzy control; least squares approximations; multivariable control systems; optimisation; pattern clustering; state-space methods; I-O representation; LS methods; Sugeno-type fuzzy models; adaptive set point filter; closed methodology; dynamic process data; equivalent fuzzy state-space representation; fuzzy clustering; industrial hydraulic linear drive; multivariate membership functions; optimization methods; steady state errors; Aerospace electronics; Closed loop systems; Damping; Data models; Predictive models; Process control; Steady-state; Fuzzy modelling; fuzzy state models; fuzzy state-space controllers;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 1999 European

Conference_Location

Karlsruhe

Print_ISBN

978-3-9524173-5-5

Type

conf

Filename

7099883